ReviewSensors (Basel, Switzerland)2025
A Review of Multi-Sensor Fusion in Autonomous Driving.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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Who cites it
19 citing papers in PubMed.
- Cyberism Perception-Interaction Layer: A Framework for AI-Enabled Sensing Between Humans and Cyberspace.Sensors (Basel, Switzerland) · 2026Review
- Sensor-Driven World Models for Embodied Intelligence: A Survey of Sensor-State-Decision Modeling Across Far-Field and Near-Field Regimes.Sensors (Basel, Switzerland) · 2026Article
- Architecture-Level Event-Oriented Frontend for Heterogeneous Sensor Inference in Photonic Stochastic Systems.Sensors (Basel, Switzerland) · 2026Article
- ARMOR: Pareto-Aware Dynamic Routing for Energy-Efficient Heterogeneous Multimodal Perception in Autonomous Driving.Annals of the New York Academy of Sciences · 2026Article
- Construction Method of Multimodal 4D Imaging Radar Dataset for Three-Dimensional Traffic Scenes.Sensors (Basel, Switzerland) · 2026Article
- Event-Based Safety Indicator Analysis of a Surround View Monitoring (SVM)-Based Pedestrian Detection System Using Real-World Urban Data.Sensors (Basel, Switzerland) · 2026Article
- An End-to-End Trajectory Prediction Method for Unmanned Ground Vehicles via Multimodal Fusion.Sensors (Basel, Switzerland) · 2026Article
- Non-Line-of-Sight Perception Method for Autonomous Haul Trucks in Open-Pit Mines Based on 4D mmWave Radar and LiDAR Fusion.Sensors (Basel, Switzerland) · 2026Article
- Advancing Egg Freshness Evaluation with Integrated AI and Spectroscopy.Foods (Basel, Switzerland) · 2026Review
- TrackRefine: A Plug-and-Play Decoupled Enhancement Framework for Online Multi-Object Tracking and Segmentation.Sensors (Basel, Switzerland) · 2026Article
- Multimodal Sensor Fusion in Autonomous Vehicles: Technologies, Architectures, and Open Challenges.Sensors (Basel, Switzerland) · 2026Review
- Development and validation of a sensor-integrated smart driving test system for automated, scalable, and objective driver performance evaluation.Scientific reports · 2026Article
- From Pixel Understanding to Semantic Insight: Intelligent Detection in Sensor-Driven Perception Systems.Sensors (Basel, Switzerland) · 2026Review
- MVDFusion: Multimodal Vehicle Detection in Foggy Weather Using LiDAR and Radar Fusion.Sensors (Basel, Switzerland) · 2026Article
- A Three-Dimensional LiDAR Observability Framework for Pedestrian Representation: Sensor Placement and Multi-View Fusion on a Compact Autonomous Vehicle.Sensors (Basel, Switzerland) · 2026Article
- Sensors in Self-Driving Vehicles: A Detailed Literature Review and New Trends.Sensors (Basel, Switzerland) · 2026Review
- Free Space Estimation Based on Superpixel Clustering for Assisted Driving.Sensors (Basel, Switzerland) · 2026Article
- Precipitation-aware sensor ecosystem modelling for performance-driven autonomous vehicle navigation.Scientific reports · 2026Article
- Context-Aware Semantic Localization with Adaptive Sensor Fusion Under Adverse Conditions.Sensors (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multi-modal sensor fusion has become a cornerstone of robust autonomous driving systems, enabling perception models to integrate complementary cues from cameras, LiDARs, radars, and other modalities. This survey provides a structured overview of recent advances in deep learning-based fusion methods, categorizing them by architectural paradigms (e.g., BEV-centric fusion and cross-modal attention), learning strategies, and task adaptations. We highlight two dominant architectural trends: unified BEV representation and token-level cross-modal alignment, analyzing their design trade-offs and integration challenges. Furthermore, we review a wide range of applications, from object detection and semantic segmentation to behavior prediction and planning. Despite considerable progress, real-world deployment is hindered by issues such as spatio-temporal misalignment, domain shifts, and limited interpretability. We discuss how recent developments, such as diffusion models for generative fusion, Mamba-style recurrent architectures, and large vision-language models, may unlock future directions for scalable and trustworthy perception systems. Extensive comparisons, benchmark analyses, and design insights are provided to guide future research in this rapidly evolving field.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.